Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Steven M Foltz, Casey S Greene, Jaclyn N Taroni

February 2023 Commun Biol

Synopsis of Social media discussions

The discussions reflect strong support and enthusiasm, with phrases like 'effective cross-platform normalization' and references to 'machine learning applications' showing appreciation for the technical advances. The tone underscores both interest and belief in the high impact of the research, with examples illustrating how the methods enable more accurate data integration in biomedical studies.

A
Agreement
 Moderate agreement

Most discussions recognize the publication's findings as valuable, highlighting effective integration methods for genomic data, which they generally support.

I
Interest
 High level of interest

The comments demonstrate high interest, with many emphasizing the significance of combining microarray and RNA-seq data for advanced research and machine learning.

E
Engagement
 High engagement

Multiple discussions delve into technical aspects, mentioning specific algorithms, normalization techniques, and potential applications, reflecting deep engagement.

I
Impact
 High level of impact

Participants view this research as highly impactful, often describing it as a 'game changer' or essential for future biomedical data analysis, emphasizing its transformative potential.

Social Mentions

YouTube

1 Videos

Twitter

16 Posts

Blogs

2 Articles

News

2 Articles

Reddit

2 Posts

Metrics

Video Views

29

Total Likes

21

Extended Reach

44,892

Social Features

23

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Cross-Platform Normalization for Microarray and RNAseq Data in Machine Learning

Cross-Platform Normalization for Microarray and RNAseq Data in Machine Learning

Cross-platform normalization techniques like quantile normalization and Training Distribution Matching enable the combined use of microarray and RNAseq data for machine learning, addressing differences in data structure and distribution.

Social RTCL TV

June 28, 2023

29 views

  • Cannock Private Limited
    @CannockPvtLtd (Twitter)

    ...https://t.co/5N2bHHC1Vw #AICompany #CannockPrivateLimited
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    March 8, 2023

  • Cannock Private Limited
    @CannockPvtLtd (Twitter)

    c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. ... https://t.co/5N2bHHC1Vw
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    March 8, 2023

  • Joel Atallah
    @joelatallah (Twitter)

    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 5, 2023

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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 2, 2023

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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 1, 2023

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  • zhenzhen wang
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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 1, 2023

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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 1, 2023

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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    RT @RNASeqBlog: Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existi…
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    March 1, 2023

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  • RNA-Seq Blog
    @RNASeqBlog (Twitter)

    Researchers at @PennMedicine demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine #microarray and #RNAseq data for #machinelearning applications. https://t.co/wwHQLbCCMN
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    March 1, 2023

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  • Analytics 4 Everyone LLC
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    Cross-platform normalization enables machine learning model training on microarray and RNA-seq data ... - https://t.co/uEexHyWcwo - Follow @A4ETechnologies for more info #ai #machinelearning https://t.co/7OdnTT3g9X
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    February 28, 2023

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    February 26, 2023

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  • Oncology & Machine Learning
    @MlOncology (Twitter)

    Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously https://t.co/UTTpzrTtiR
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    February 26, 2023

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    Cross-platform normalization enables machine learning model training on microarray - https://t.co/KyGCDZJ79w #machinelearning #intoAInews
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    February 25, 2023

  • Paul Lopez
    @lopezunwired (Twitter)

    Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously https://t.co/w5G4GDhsit #MachineLearning #NatureJournal #AI https://t.co/eut9CsjwqP
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    February 25, 2023

Abstract Synopsis

  • Cross-platform normalization techniques like quantile normalization and Training Distribution Matching enable the combined use of microarray and RNAseq data for machine learning, addressing differences in data structure and distribution.
  • Supervised and unsupervised machine learning evaluations show that certain normalization methods can effectively facilitate the integration of the two platforms for predictive modeling.
  • Alternative methods such as nonparanormal normalization and z-scores are also useful in specific contexts, like pathway analysis with PLIER, demonstrating the potential for combining diverse gene expression datasets in biomedical research.